Hybrid Conferencee

International Conference on Computational Data Analysis and Modeling Techniques (ICCDAMT - 26)

10th - 11th October 2026 | Milan, Italy

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Conference Notifications:

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Milan. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Milan event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Milan conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Milan, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Milan."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICCDAMT aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Computational techniques for data analysis
02
Modeling approaches in data science applications
03
Big data analytics and computational methods
04
Machine learning algorithms for data modeling
05
Data visualization techniques for analysis
06
Statistical methods in computational data analysis
07
Challenges in real-time data modeling
08
Interdisciplinary approaches to data analysis
09
Future trends in computational data techniques
10
Case studies on successful data modeling
11
Ethics in data analysis and modeling
12
Innovative computational tools for data science
13
Data mining techniques for large datasets
14
Impact of AI on data analysis methods
15
User-centric design in data analysis tools
16
Applications of computational methods in business
17
Security considerations in data analysis
18
Frameworks for evaluating data modeling approaches
19
Collaborative data analysis techniques
20
Real-world applications of computational data analysis